Can shoulder range of movement be measured accurately using the Microsoft Kinect sensor plus Medical Interactive Recovery Assistant (MIRA) software?

BackgroundThis study compared the accuracy of measuring shoulder range of movement (ROM) with a simple laptop-sensor combination vs. trained observers (shoulder physiotherapists and shoulder surgeons) using motion capture (MoCap) laboratory equipment as the gold standard. MethodsThe Microsoft Kinect sensor (Microsoft Corp., Redmond, WA, USA) tracks 3-dimensional human motion. Ordinarily used with an Xbox (Microsoft Corp.) video game console, Medical Interactive Recovery Assistant (MIRA) software (MIRA Rehab Ltd., London, UK) allows this small sensor to measure shoulder movement with a standard computer. Shoulder movements of 49 healthy volunteers were simultaneously measured by trained observers, MoCap, and the MIRA device. Internal rotation was assessed with the shoulder abducted 90° and external rotation with the shoulder adducted. Visual estimation and MIRA measurements were compared with gold standard MoCap measurements for agreement using Bland-Altman methods. Results There were 1670 measurements analyzed. The MIRA evaluations of all 4 cardinal shoulder movements were significantly more precise, with narrower limits of agreement, than the measurements of trained observers. MIRA achieved ±11° (95% confidence interval [CI], 8.7°-12.6°) for forward flexion vs. ±16° (95% CI, 14.6°-17.6°) by trained observers. For abduction, MIRA showed ±11° (95% CI, 8.7°-12.8°) against ±15° (95% CI, 13.4°-16.2°) for trained observers. MIRA attained ±10° (95% CI, 8.1°-11.9°) during external rotation measurement, whereas trained observers only reached ±21° (95% CI, 18.7°-22.6°). For internal rotation, MIRA achieved ±9° (95% CI, 7.2°-10.4°), which was again better than TOs at ±18° (95% CI, 16.0°-19.3°). ConclusionsA laptop combined with a Microsoft Kinect sensor and the MIRA software can measure shoulder movements with acceptable levels of accuracy. This technology, which can be easily set up, may also allow precise shoulder ROM measurement outside the clinic setting

Measurement of Upper Limb Range of Motion Using Wearable Sensors: A Systematic Review.

Background: Wearable sensors are portable measurement tools that are becoming increasingly popular for the measurement of joint angle in the upper limb. With many brands emerging on the market, each with variations in hardware and protocols, evidence to inform selection and application is needed. Therefore, the objectives of this review were related to the use of wearable sensors to calculate upper limb joint angle. We aimed to describe (i) the characteristics of commercial and custom wearable sensors, (ii) the populations for whom researchers have adopted wearable sensors, and (iii) their established psychometric properties. Methods: A systematic review of literature was undertaken using the following data bases: MEDLINE, EMBASE, CINAHL, Web of Science, SPORTDiscus, IEEE, and Scopus. Studies were eligible if they met the following criteria: (i) involved humans and/or robotic devices, (ii) involved the application or simulation of wearable sensors on the upper limb, and (iii) calculated a joint angle. Results: Of 2191 records identified, 66 met the inclusion criteria. Eight studies compared wearable sensors to a robotic device and 22 studies compared to a motion analysis system. Commercial (n = 13) and custom (n = 7) wearable sensors were identified, each with variations in placement, calibration methods, and fusion algorithms, which were demonstrated to influence accuracy. Conclusion: Wearable sensors have potential as viable instruments for measurement of joint angle in the upper limb during active movement. Currently, customised application (i.e. calibration and angle calculation methods) is required to achieve sufficient accuracy (error < 5°). Additional research and standardisation is required to guide clinical application

Can shoulder range of movement be measured accurately using the Microsoft Kinect sensor plus Medical Interactive Recovery Assistant software?

© 2017 Journal of Shoulder and Elbow Surgery Board of Trustees. Background: This study compared the accuracy of measuring shoulder range of movement (ROM) with a simple laptop-sensor combination vs. trained observers (shoulder physiotherapists and shoulder surgeons) using motion capture (MoCap) laboratory equipment as the gold standard. Methods: The Microsoft Kinect sensor (Microsoft Corp., Redmond, WA, USA) tracks 3-dimensional human motion. Ordinarily used with an Xbox (Microsoft Corp.) video game console, Medical Interactive Recovery Assistant (MIRA) software (MIRA Rehab Ltd., London, UK) allows this small sensor to measure shoulder movement with a standard computer. Shoulder movements of 49 healthy volunteers were simultaneously measured by trained observers, MoCap, and the MIRA device. Internal rotation was assessed with the shoulder abducted 90° and external rotation with the shoulder adducted. Visual estimation and MIRA measurements were compared with gold standard MoCap measurements for agreement using Bland-Altman methods. Results: There were 1670 measurements analyzed. The MIRA evaluations of all 4 cardinal shoulder movements were significantly more precise, with narrower limits of agreement, than the measurements of trained observers. MIRA achieved ±11° (95% confidence interval [CI], 8.7°-12.6°) for forward flexion vs. ±16° (95% CI, 14.6°-17.6°) by trained observers. For abduction, MIRA showed ±11° (95% CI, 8.7°-12.8°) against ±15° (95% CI, 13.4°-16.2°) for trained observers. MIRA attained ±10° (95% CI, 8.1°-11.9°) during external rotation measurement, whereas trained observers only reached ±21° (95% CI, 18.7°-22.6°). For internal rotation, MIRA achieved ±9° (95% CI, 7.2°-10.4°), which was again better than TOs at ±18° (95% CI, 16.0°-19.3°). Conclusions: A laptop combined with a Microsoft Kinect sensor and the MIRA software can measure shoulder movements with acceptable levels of accuracy. This technology, which can be easily set up, may also allow precise shoulder ROM measurement outside the clinic setting

Leveraging Activity Recognition to Enable Protective Behavior Detection in Continuous Data

Protective behavior exhibited by people with chronic pain (CP) during physical activities is the key to understanding their physical and emotional states. Existing automatic protective behavior detection (PBD) methods rely on pre-segmentation of activities predefined by users. However, in real life, people perform activities casually. Therefore, where those activities present difficulties for people with chronic pain, technology-enabled support should be delivered continuously and automatically adapted to activity type and occurrence of protective behavior. Hence, to facilitate ubiquitous CP management, it becomes critical to enable accurate PBD over continuous data. In this paper, we propose to integrate human activity recognition (HAR) with PBD via a novel hierarchical HAR-PBD architecture comprising graph-convolution and long short-term memory (GC-LSTM) networks, and alleviate class imbalances using a class-balanced focal categorical-cross-entropy (CFCC) loss. Through in-depth evaluation of the approach using a CP patients' dataset, we show that the leveraging of HAR, GC-LSTM networks, and CFCC loss leads to clear increase in PBD performance against the baseline (macro F1 score of 0.81 vs. 0.66 and precision-recall area-under-the-curve (PR-AUC) of 0.60 vs. 0.44). We conclude by discussing possible use cases of the hierarchical architecture in CP management and beyond. We also discuss current limitations and ways forward.Comment: Submitted to PACM IMWU

Quantifying the Effects of Knee Joint Biomechanics on Acoustical Emissions

The knee is one of the most injured body parts, causing 18 million patients to be seen in clinics every year. Because the knee is a weight-bearing joint, it is prone to pathologies such as osteoarthritis and ligamentous injuries. Existing technologies for monitoring knee health can provide accurate assessment and diagnosis for acute injuries. However, they are mainly confined to clinical or laboratory settings only, time-consuming, expensive, and not well-suited for longitudinal monitoring. Developing a novel technology for joint health assessment beyond the clinic can further provide insights on the rehabilitation process and quantitative usage of the knee joint. To better understand the underlying properties and fundamentals of joint sounds, this research will investigate the relationship between the changes in the knee joint structure (i.e. structural damage and joint contact force) and the JAEs while developing novel techniques for analyzing these sounds. We envision that the possibility of quantifying joint structure and joint load usage from these acoustic sensors would advance the potential of JAE as the next biomarker of joint health that can be captured with wearable technology. First, we developed a novel processing technique for JAEs that quantify on the structural change of the knee from injured athletes and human lower-limb cadaver models. Second, we quantified whether JAEs can detect the increase in the mechanical stress on the knee joint using an unsupervised graph mining algorithm. Lastly, we quantified the directional bias of the load distribution between medial and lateral compartment using JAEs. Understanding and monitoring the quantitative usage of knee loads in daily activities can broaden the implications for longitudinal joint health monitoring.Ph.D

Protective Behavior Detection in Chronic Pain Rehabilitation: From Data Preprocessing to Learning Model

Chronic pain (CP) rehabilitation extends beyond physiotherapist-directed clinical sessions and primarily functions in people's everyday lives. Unfortunately, self-directed rehabilitation is difficult because patients need to deal with both their pain and the mental barriers that pain imposes on routine functional activities. Physiotherapists adjust patients' exercise plans and advice in clinical sessions based on the amount of protective behavior (i.e., a sign of anxiety about movement) displayed by the patient. The goal of such modifications is to assist patients in overcoming their fears and maintaining physical functioning. Unfortunately, physiotherapists' support is absent during self-directed rehabilitation or also called self-management that people conduct in their daily life. To be effective, technology for chronic-pain self-management should be able to detect protective behavior to facilitate personalized support. Thereon, this thesis addresses the key challenges of ubiquitous automatic protective behavior detection (PBD). Our investigation takes advantage of an available dataset (EmoPain) containing movement and muscle activity data of healthy people and people with CP engaged in typical everyday activities. To begin, we examine the data augmentation methods and segmentation parameters using various vanilla neural networks in order to enable activity-independent PBD within pre-segmented activity instances. Second, by incorporating temporal and bodily attention mechanisms, we improve PBD performance and support theoretical/clinical understanding of protective behavior that the attention of a person with CP shifts between body parts perceived as risky during feared movements. Third, we use human activity recognition (HAR) to improve continuous PBD in data of various activity types. The approaches proposed above are validated against the ground truth established by majority voting from expert annotators. Unfortunately, using such majority-voted ground truth causes information loss, whereas direct learning from all annotators is vulnerable to noise from disagreements. As the final study, we improve the learning from multiple annotators by leveraging the agreement information for regularization

Personalised exercise recognition towards improved self-management of musculoskeletal disorders.

Musculoskeletal Disorders (MSD) have been the primary contributor to the global disease burden, with increased years lived with disability. Such chronic conditions require self-management, typically in the form of maintaining an active lifestyle while adhering to prescribed exercises. Today, exercise monitoring in fitness applications wholly relies on user input. Effective digital intervention for self-managing MSD should be capable of monitoring, recognising and assessing performance quality of exercises in real-time. Exercise Recognition (ExRec) is the machine learning problem that investigates the automation of exercise monitoring. Multiple challenges arise when implementing high performing ExRec algorithms for a wide range of exercises performed by people from different demographics. In this thesis, we explore three personalisation challenges. Different sensor combinations can be used to capture exercises, to improve usability and deployability in restricted settings. Accordingly, a recognition algorithm should be adaptable to different sensor combinations. To address this challenge, we investigate the best feature learners for individual sensors, and effective fusion methods that minimise the need for data and very deep architectures. We implement a modular hybrid attention fusion architecture that emphasises significant features and understates noisy features from multiple sensors for each exercise. Persons perform exercises differently when not supervised; they incorporate personal rhythms and nuances. Accordingly, a recognition algorithm should be able to adapt to different persons. To address the personalised recognition challenge, we investigate how to adapt learned models to new, unseen persons. Key to achieving effective personalisation is the ability to personalise with few data instances. Accordingly, we bring together personalisation methods and advances in meta-learning to introduce personalised meta-learning methodology. The resulting personalised meta-learners are learning to adapt to new end-users with only few data instances. It is infeasible to design algorithms to recognise all expected exercises a physiotherapist would prescribe. Accordingly, the ability to integrate new exercises after deployment is another challenge in ExRec. The challenge of adapting to unseen exercises is known as open-ended recognition. We extend the personalised meta-learning methodology to the open-ended domain, such that an end-user can introduce a new exercise to the model with only a few data instances. Finally, we address the lack of publicly available data and collaborate with health science researchers to curate a heterogeneous multi-modal physiotherapy exercise dataset, MEx. We conduct comprehensive evaluations of the proposed methods using MEx to demonstrate that our methods successfully address the three ExRec challenges. We also show that our contributions are not restricted to the domain of ExRec, but are applicable in a wide range of activity recognition tasks by extending the evaluation to other human activity recognition domains

Guidage non-intrusif d'un bras robotique à l'aide d'un bracelet myoélectrique à électrode sèche

Depuis plusieurs années la robotique est vue comme une solution clef pour améliorer la qualité de vie des personnes ayant subi une amputation. Pour créer de nouvelles prothèses intelligentes qui peuvent être facilement intégrées à la vie quotidienne et acceptée par ces personnes, celles-ci doivent être non-intrusives, fiables et peu coûteuses. L’électromyographie de surface fournit une interface intuitive et non intrusive basée sur l’activité musculaire de l’utilisateur permettant d’interagir avec des robots. Cependant, malgré des recherches approfondies dans le domaine de la classification des signaux sEMG, les classificateurs actuels manquent toujours de fiabilité, car ils ne sont pas robustes face au bruit à court terme (par exemple, petit déplacement des électrodes, fatigue musculaire) ou à long terme (par exemple, changement de la masse musculaire et des tissus adipeux) et requiert donc de recalibrer le classifieur de façon périodique. L’objectif de mon projet de recherche est de proposer une interface myoélectrique humain-robot basé sur des algorithmes d’apprentissage par transfert et d’adaptation de domaine afin d’augmenter la fiabilité du système à long-terme, tout en minimisant l’intrusivité (au niveau du temps de préparation) de ce genre de système. L’aspect non intrusif est obtenu en utilisant un bracelet à électrode sèche possédant dix canaux. Ce bracelet (3DC Armband) est de notre (Docteur Gabriel Gagnon-Turcotte, mes co-directeurs et moi-même) conception et a été réalisé durant mon doctorat. À l’heure d’écrire ces lignes, le 3DC Armband est le bracelet sans fil pour l’enregistrement de signaux sEMG le plus performant disponible. Contrairement aux dispositifs utilisant des électrodes à base de gel qui nécessitent un rasage de l’avant-bras, un nettoyage de la zone de placement et l’application d’un gel conducteur avant l’utilisation, le brassard du 3DC peut simplement être placé sur l’avant-bras sans aucune préparation. Cependant, cette facilité d’utilisation entraîne une diminution de la qualité de l’information du signal. Cette diminution provient du fait que les électrodes sèches obtiennent un signal plus bruité que celle à base de gel. En outre, des méthodes invasives peuvent réduire les déplacements d’électrodes lors de l’utilisation, contrairement au brassard. Pour remédier à cette dégradation de l’information, le projet de recherche s’appuiera sur l’apprentissage profond, et plus précisément sur les réseaux convolutionels. Le projet de recherche a été divisé en trois phases. La première porte sur la conception d’un classifieur permettant la reconnaissance de gestes de la main en temps réel. La deuxième porte sur l’implémentation d’un algorithme d’apprentissage par transfert afin de pouvoir profiter des données provenant d’autres personnes, permettant ainsi d’améliorer la classification des mouvements de la main pour un nouvel individu tout en diminuant le temps de préparation nécessaire pour utiliser le système. La troisième phase consiste en l’élaboration et l’implémentation des algorithmes d’adaptation de domaine et d’apprentissage faiblement supervisé afin de créer un classifieur qui soit robuste au changement à long terme.For several years, robotics has been seen as a key solution to improve the quality of life of people living with upper-limb disabilities. To create new, smart prostheses that can easily be integrated into everyday life, they must be non-intrusive, reliable and inexpensive. Surface electromyography provides an intuitive interface based on a user’s muscle activity to interact with robots. However, despite extensive research in the field of sEMG signal classification, current classifiers still lack reliability due to their lack of robustness to short-term (e.g. small electrode displacement, muscle fatigue) or long-term (e.g. change in muscle mass and adipose tissue) noise. In practice, this mean that to be useful, classifier needs to be periodically re-calibrated, a time consuming process. The goal of my research project is to proposes a human-robot myoelectric interface based on transfer learning and domain adaptation algorithms to increase the reliability of the system in the long term, while at the same time reducing the intrusiveness (in terms of hardware and preparation time) of this kind of systems. The non-intrusive aspect is achieved from a dry-electrode armband featuring ten channels. This armband, named the 3DC Armband is from our (Dr. Gabriel Gagnon-Turcotte, my co-directors and myself) conception and was realized during my doctorate. At the time of writing, the 3DC Armband offers the best performance for currently available dry-electrodes, surface electromyographic armbands. Unlike gel-based electrodes which require intrusive skin preparation (i.e. shaving, cleaning the skin and applying conductive gel), the 3DC Armband can simply be placed on the forearm without any preparation. However, this ease of use results in a decrease in the quality of information. This decrease is due to the fact that the signal recorded by dry electrodes is inherently noisier than gel-based ones. In addition, other systems use invasive methods (intramuscular electromyography) to capture a cleaner signal and reduce the source of noises (e.g. electrode shift). To remedy this degradation of information resulting from the non-intrusiveness of the armband, this research project will rely on deep learning, and more specifically on convolutional networks. The research project was divided into three phases. The first is the design of a classifier allowing the recognition of hand gestures in real-time. The second is the implementation of a transfer learning algorithm to take advantage of the data recorded across multiple users, thereby improving the system’s accuracy, while decreasing the time required to use the system. The third phase is the development and implementation of a domain adaptation and self-supervised learning to enhance the classifier’s robustness to long-term changes

Automated Intelligent Cueing Device to Improve Ambient Gait Behaviors for Patients with Parkinson\u27s Disease

Freezing of gait (FoG) is a common motor dysfunction in individuals with Parkinson’s disease (PD). FoG impairs walking and is associated with increased fall risk. Although pharmacological treatments have shown promise during ON-medication periods, FoG remains difficult to treat during medication OFF state and in advanced stages of the disease. External cueing therapy in the forms of visual, auditory, and vibrotactile, has been effective in treating gait deviations. Intelligent (or on-demand) cueing devices are novel systems that analyze gait patterns in real-time and activate cues only at moments when specific gait alterations are detected. In this study we developed methods to analyze gait signals collected through wearable sensors and accurately identify FoG episodes. We also investigated the potential of predicting the symptoms before their actual occurrence. We collected data from seven participants with PD using two Inertial Measurement Units (IMUs) on ankles. In our first study, we extracted engineered features from the signals and used machine learning (ML) methods to identify FoG episodes. We tested the performance of models using patient-dependent and patient-independent paradigms. The former models achieved 92.5% and 89.0% for average sensitivity and specificity, respectively. However, the conventional binary classification methods fail to accurately classify data if only data from normal gait periods are available. In order to identify FoG episodes in participants who did not freeze during data collection sessions, we developed a Deep Gait Anomaly Detector (DGAD) to identify anomalies (i.e., FoG) in the signals. DGAD was formed of convolutional layers and trained to automatically learn features from signals. The convolutional layers are followed by fully connected layers to reduce the dimensions of the features. A k-nearest neighbors (kNN) classifier is then used to classify the data as normal or FoG. The models identified 87.4% of FoG onsets, with 21.9% being predicted on average for each participant. This study demonstrates our algorithm\u27s potential for delivery of preventive cues. The DGAD algorithm was then implemented in an Android application to monitor gait patterns of PD patients in ambient environments. The phone triggered vibrotactile and auditory cues on a connected smartwatch if an FoG episode was identified. A 6-week in-home study showed the potentials for effective treatment of FoG severity in ambient environments using intelligent cueing devices